Alpha_div_tax
Mauricio Hernández
Laboratory of Biotic Interactions, Centro de Investigación en Ciencias Biológicas, Universidad Autónoma de Tlaxcala, Tlaxcala, Mexicodelmer.hernandez@unah.edu.hn
Stephanie Hereira-Pacheco
Laboratory of Biotic Interactions, Centro de Investigación en Ciencias Biológicas, Universidad Autónoma de Tlaxcala, Tlaxcala, Mexicoshereirap@gmail.com
Sergio Ancona
Departamento de Ecología Evolutiva, Instituto de Ecología, Universidad Nacional Autónoma de Méxicosergio.ancona@iecologia.unam.mx
Yendi E. Navarro-Noya
Laboratory of Biotic Interactions, Centro de Investigación en Ciencias Biológicas, Universidad Autónoma de Tlaxcala, Tlaxcala, Mexicoyendiebenezer.navarro.n@uatx.mx
28 - 03 - 2024
II. ALPHA DIVERSITY ANALYSES
Loading libraries
Calculating Hill numbers with hilldiv R package
Preparing the data
Load files
Declare factors
Evaluate whether sex variable influence on gut microbiota diversity (q=1)
# Paired test (Wilcoxon test, q1)
# Sceloporus aeneus
aeneus_fem_GutMic <- subset(Micro_div, Species_Scel == "Sceloporus aeneus" &
Sex == "Female", q1, drop = TRUE)
aeneus_male_GutMic <- subset(Micro_div, Species_Scel == "Sceloporus aeneus" &
Sex == "Male", q1, drop = TRUE)
aeneus_FemMale_GutMic <- wilcox.test(x= aeneus_fem_GutMic, y= aeneus_male_GutMic)
aeneus_FemMale_GutMic##
## Wilcoxon rank sum exact test
##
## data: aeneus_fem_GutMic and aeneus_male_GutMic
## W = 49, p-value = 0.4779
## alternative hypothesis: true location shift is not equal to 0# Sceloporus bicanthalis
bica_fem_GutMic <- subset(Micro_div, Species_Scel == "Sceloporus bicanthalis" &
Sex == "Female", q1, drop = TRUE)
bica_male_GutMic <- subset(Micro_div, Species_Scel == "Sceloporus bicanthalis" &
Sex == "Male", q1, drop = TRUE)
bica_FemMale_GutMic <- wilcox.test(x= bica_fem_GutMic, y= bica_male_GutMic)
bica_FemMale_GutMic##
## Wilcoxon rank sum exact test
##
## data: bica_fem_GutMic and bica_male_GutMic
## W = 69, p-value = 0.9095
## alternative hypothesis: true location shift is not equal to 0# Sceloporus grammicus
gram_fem_GutMic <- subset(Micro_div, Species_Scel == "Sceloporus grammicus" &
Sex == "Female", q1, drop = TRUE)
gram_male_GutMic <- subset(Micro_div, Species_Scel == "Sceloporus grammicus" &
Sex == "Male", q1, drop = TRUE)
gram_FemMale_GutMic <- wilcox.test(x= gram_fem_GutMic, y= gram_male_GutMic)
gram_FemMale_GutMic##
## Wilcoxon rank sum exact test
##
## data: gram_fem_GutMic and gram_male_GutMic
## W = 109, p-value = 0.5676
## alternative hypothesis: true location shift is not equal to 0# Sceloporus spinosus
spi_fem_GutMic <- subset(Micro_div, Species_Scel == "Sceloporus spinosus" &
Sex == "Female", q1, drop = TRUE)
spi_male_GutMic <- subset(Micro_div, Species_Scel == "Sceloporus spinosus" &
Sex == "Male", q1, drop = TRUE)
spi_FemMale_GutMic <- wilcox.test(x= spi_fem_GutMic, y= spi_male_GutMic)
spi_FemMale_GutMic##
## Wilcoxon rank sum exact test
##
## data: spi_fem_GutMic and spi_male_GutMic
## W = 55, p-value = 0.8596
## alternative hypothesis: true location shift is not equal to 0Differences in snout vent length (SVL) among lizard species
set.seed(1234)
Data_SVL <- subset(Micro_div, select = c(Species_Scel, SVL))
kruskal.t <- Data_SVL %>% kruskal_test(SVL ~ Species_Scel)
kruskal.tVisualization: box plots with p-values
Post_hoc <- Post_hoc %>%
add_xy_position(x = "Species_Scel")
ggboxplot(Data_SVL, x = "Species_Scel", y = "SVL", fill = "Species_Scel") +
xlab(element_blank())+
scale_fill_manual(values = c("#56B4E9","#009E73", "#999999","#E69F00"))+
theme_classic() +
theme(legend.position = "right",
axis.ticks.x = element_blank(),
axis.text.x = element_blank(),
legend.title = element_blank()) +
theme(legend.text = element_text(face = "italic"))+
stat_pvalue_manual(Post_hoc, hide.ns = TRUE) +
labs(
subtitle = get_test_label(kruskal.t, detailed = TRUE),
caption = get_pwc_label(Post_hoc))
Generalized Linear Model with quasi-Poisson distribution (q = 1)
M1 <- glm(q1 ~ SEASON*SPECIES+ELEVATION+SVL+SEQDEPTH,
family = quasipoisson(link = "log"),
data = Micro_div)
summary(M1)##
## Call:
## glm(formula = q1 ~ SEASON * SPECIES + ELEVATION + SVL + SEQDEPTH,
## family = quasipoisson(link = "log"), data = Micro_div)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.262828 0.228362 18.667 < 2e-16
## SEASONRainy 0.405260 0.151684 2.672 0.009061
## SPECIESSceloporus bicanthalis 0.989264 0.459589 2.152 0.034227
## SPECIESSceloporus grammicus 0.028572 0.168483 0.170 0.865747
## SPECIESSceloporus spinosus 0.044405 0.217178 0.204 0.838486
## ELEVATION -0.387007 0.399665 -0.968 0.335661
## SVL 0.002237 0.003986 0.561 0.576205
## SEQDEPTH 0.235518 0.065659 3.587 0.000561
## SEASONRainy:SPECIESSceloporus bicanthalis -0.641160 0.190178 -3.371 0.001132
## SEASONRainy:SPECIESSceloporus grammicus -0.249934 0.196457 -1.272 0.206811
## SEASONRainy:SPECIESSceloporus spinosus -0.239895 0.202098 -1.187 0.238567
##
## (Intercept) ***
## SEASONRainy **
## SPECIESSceloporus bicanthalis *
## SPECIESSceloporus grammicus
## SPECIESSceloporus spinosus
## ELEVATION
## SVL
## SEQDEPTH ***
## SEASONRainy:SPECIESSceloporus bicanthalis **
## SEASONRainy:SPECIESSceloporus grammicus
## SEASONRainy:SPECIESSceloporus spinosus
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 11.08586)
##
## Null deviance: 1537.22 on 94 degrees of freedom
## Residual deviance: 999.94 on 84 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
##
## Call:
## glm(formula = q1 ~ SEASON + SPECIES + ELEVATION + SVL + SEQDEPTH,
## family = quasipoisson(link = "log"), data = Micro_div)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.462617 0.218130 20.458 < 2e-16 ***
## SEASONRainy 0.114355 0.078245 1.462 0.14748
## SPECIESSceloporus bicanthalis 0.652287 0.476593 1.369 0.17463
## SPECIESSceloporus grammicus -0.144594 0.116591 -1.240 0.21824
## SPECIESSceloporus spinosus -0.114180 0.186455 -0.612 0.54189
## ELEVATION -0.467503 0.427006 -1.095 0.27661
## SVL 0.001759 0.004152 0.424 0.67284
## SEQDEPTH 0.205901 0.068543 3.004 0.00348 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 12.24543)
##
## Null deviance: 1537.2 on 94 degrees of freedom
## Residual deviance: 1136.0 on 87 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = q1 ~ SEASON + SPECIES + ELEVATION + SEQDEPTH, family = quasipoisson(link = "log"),
## data = Micro_div)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.53327 0.14023 32.327 < 2e-16 ***
## SEASONRainy 0.11649 0.07772 1.499 0.13747
## SPECIESSceloporus bicanthalis 0.66616 0.47539 1.401 0.16464
## SPECIESSceloporus grammicus -0.11718 0.09653 -1.214 0.22803
## SPECIESSceloporus spinosus -0.04897 0.10416 -0.470 0.63943
## ELEVATION -0.48877 0.42406 -1.153 0.25220
## SEQDEPTH 0.20354 0.06796 2.995 0.00356 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 12.13221)
##
## Null deviance: 1537.2 on 94 degrees of freedom
## Residual deviance: 1138.2 on 88 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = q1 ~ SEASON + SPECIES + SEQDEPTH, family = quasipoisson(link = "log"),
## data = Micro_div)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.66137 0.08481 54.964 <2e-16 ***
## SEASONRainy 0.11993 0.07821 1.533 0.1287
## SPECIESSceloporus bicanthalis 0.12860 0.09341 1.377 0.1721
## SPECIESSceloporus grammicus -0.11600 0.09699 -1.196 0.2349
## SPECIESSceloporus spinosus -0.01756 0.10109 -0.174 0.8625
## SEQDEPTH 0.20435 0.06833 2.991 0.0036 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 12.2577)
##
## Null deviance: 1537.2 on 94 degrees of freedom
## Residual deviance: 1157.3 on 89 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) 4.49221597 4.82475124
## SEASONRainy -0.03336187 0.27330751
## SPECIESSceloporus bicanthalis -0.05390201 0.31251946
## SPECIESSceloporus grammicus -0.30596309 0.07452011
## SPECIESSceloporus spinosus -0.21612307 0.18046386
## SEQDEPTH 0.06781350 0.33576942##
## Call:
## glm(formula = q1 ~ SEASON + SPECIES, family = quasipoisson(link = "log"),
## data = Micro_div)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.60321 0.08660 53.155 < 2e-16 ***
## SEASONRainy 0.22149 0.07245 3.057 0.00294 **
## SPECIESSceloporus bicanthalis 0.15745 0.09734 1.618 0.10927
## SPECIESSceloporus grammicus -0.11983 0.10129 -1.183 0.23993
## SPECIESSceloporus spinosus -0.01096 0.10574 -0.104 0.91766
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 13.41224)
##
## Null deviance: 1537.2 on 94 degrees of freedom
## Residual deviance: 1260.8 on 90 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4# Best Model
M6 <- glm(q1 ~ SEASON*SPECIES,
family = quasipoisson(link = "log"),
data = Micro_div)
anova(M6, test="F")##
## Call:
## glm(formula = q1 ~ SEASON * SPECIES, family = quasipoisson(link = "log"),
## data = Micro_div)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.3930 0.1316 33.371 < 2e-16
## SEASONRainy 0.5318 0.1561 3.406 0.00100
## SPECIESSceloporus bicanthalis 0.5414 0.1656 3.270 0.00154
## SPECIESSceloporus grammicus 0.1005 0.1635 0.615 0.54032
## SPECIESSceloporus spinosus 0.1593 0.1715 0.929 0.35550
## SEASONRainy:SPECIESSceloporus bicanthalis -0.5796 0.2020 -2.869 0.00517
## SEASONRainy:SPECIESSceloporus grammicus -0.3298 0.2065 -1.597 0.11392
## SEASONRainy:SPECIESSceloporus spinosus -0.2364 0.2153 -1.098 0.27537
##
## (Intercept) ***
## SEASONRainy **
## SPECIESSceloporus bicanthalis **
## SPECIESSceloporus grammicus
## SPECIESSceloporus spinosus
## SEASONRainy:SPECIESSceloporus bicanthalis **
## SEASONRainy:SPECIESSceloporus grammicus
## SEASONRainy:SPECIESSceloporus spinosus
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 12.61408)
##
## Null deviance: 1537.2 on 94 degrees of freedom
## Residual deviance: 1151.1 on 87 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) 4.1233396 4.64031820
## SEASONRainy 0.2317199 0.84503460
## SPECIESSceloporus bicanthalis 0.2207910 0.87128269
## SPECIESSceloporus grammicus -0.2155594 0.42677045
## SPECIESSceloporus spinosus -0.1742816 0.49967406
## SEASONRainy:SPECIESSceloporus bicanthalis -0.9789785 -0.18613994
## SEASONRainy:SPECIESSceloporus grammicus -0.7385988 0.07179439
## SEASONRainy:SPECIESSceloporus spinosus -0.6616867 0.18335432## (Intercept)
## 4.3929537
## SEASONRainy
## 0.5317728
## SPECIESSceloporus bicanthalis
## 0.5413804
## SPECIESSceloporus grammicus
## 0.1005116
## SPECIESSceloporus spinosus
## 0.1593282
## SEASONRainy:SPECIESSceloporus bicanthalis
## -0.5795744
## SEASONRainy:SPECIESSceloporus grammicus
## -0.3297727
## SEASONRainy:SPECIESSceloporus spinosus
## -0.2363617Residual plot of the best model
Plot model assumption
residuals_q1 <- plot_model(M6, type = "eff", terms = c("SEASON","SPECIES"),
colors = c("#999999", "#E69F00", "#56B4E9",
"#009E73", "#F0E442")) +
theme_classic() +
theme(legend.text = element_text(face = "italic")) +
theme(axis.text.x = element_text(size = 11))
residuals_q1 
Contrasts (dry vs rainy), CI95%, p values
Tukey Test
## SPECIES SEASON rate SE df asymp.LCL asymp.UCL
## Sceloporus aeneus Dry 80.9 10.65 Inf 62.5 105
## Sceloporus bicanthalis Dry 139.0 13.96 Inf 114.1 169
## Sceloporus grammicus Dry 89.4 8.67 Inf 74.0 108
## Sceloporus spinosus Dry 94.8 10.43 Inf 76.5 118
## Sceloporus aeneus Rainy 137.7 11.56 Inf 116.8 162
## Sceloporus bicanthalis Rainy 132.5 10.56 Inf 113.3 155
## Sceloporus grammicus Rainy 109.4 10.31 Inf 91.0 132
## Sceloporus spinosus Rainy 127.4 12.68 Inf 104.9 155
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log scaleM1.emm.s <- emmeans(M6, specs = ~ SPECIES*SEASON)
pairs(M1.emm.s, adjust = "tukey", infer=c(TRUE,TRUE))## contrast estimate SE df
## Sceloporus aeneus Dry - Sceloporus bicanthalis Dry -0.54138 0.166 Inf
## Sceloporus aeneus Dry - Sceloporus grammicus Dry -0.10051 0.164 Inf
## Sceloporus aeneus Dry - Sceloporus spinosus Dry -0.15933 0.172 Inf
## Sceloporus aeneus Dry - Sceloporus aeneus Rainy -0.53177 0.156 Inf
## Sceloporus aeneus Dry - Sceloporus bicanthalis Rainy -0.49358 0.154 Inf
## Sceloporus aeneus Dry - Sceloporus grammicus Rainy -0.30251 0.162 Inf
## Sceloporus aeneus Dry - Sceloporus spinosus Rainy -0.45474 0.165 Inf
## Sceloporus bicanthalis Dry - Sceloporus grammicus Dry 0.44087 0.140 Inf
## Sceloporus bicanthalis Dry - Sceloporus spinosus Dry 0.38205 0.149 Inf
## Sceloporus bicanthalis Dry - Sceloporus aeneus Rainy 0.00961 0.131 Inf
## Sceloporus bicanthalis Dry - Sceloporus bicanthalis Rainy 0.04780 0.128 Inf
## Sceloporus bicanthalis Dry - Sceloporus grammicus Rainy 0.23887 0.138 Inf
## Sceloporus bicanthalis Dry - Sceloporus spinosus Rainy 0.08664 0.141 Inf
## Sceloporus grammicus Dry - Sceloporus spinosus Dry -0.05882 0.147 Inf
## Sceloporus grammicus Dry - Sceloporus aeneus Rainy -0.43126 0.128 Inf
## Sceloporus grammicus Dry - Sceloporus bicanthalis Rainy -0.39307 0.125 Inf
## Sceloporus grammicus Dry - Sceloporus grammicus Rainy -0.20200 0.135 Inf
## Sceloporus grammicus Dry - Sceloporus spinosus Rainy -0.35423 0.139 Inf
## Sceloporus spinosus Dry - Sceloporus aeneus Rainy -0.37244 0.138 Inf
## Sceloporus spinosus Dry - Sceloporus bicanthalis Rainy -0.33425 0.136 Inf
## Sceloporus spinosus Dry - Sceloporus grammicus Rainy -0.14318 0.145 Inf
## Sceloporus spinosus Dry - Sceloporus spinosus Rainy -0.29541 0.148 Inf
## Sceloporus aeneus Rainy - Sceloporus bicanthalis Rainy 0.03819 0.116 Inf
## Sceloporus aeneus Rainy - Sceloporus grammicus Rainy 0.22926 0.126 Inf
## Sceloporus aeneus Rainy - Sceloporus spinosus Rainy 0.07703 0.130 Inf
## Sceloporus bicanthalis Rainy - Sceloporus grammicus Rainy 0.19107 0.123 Inf
## Sceloporus bicanthalis Rainy - Sceloporus spinosus Rainy 0.03884 0.127 Inf
## Sceloporus grammicus Rainy - Sceloporus spinosus Rainy -0.15223 0.137 Inf
## asymp.LCL asymp.UCL z.ratio p.value
## -1.0432 -0.0396 -3.270 0.0239
## -0.5961 0.3950 -0.615 0.9987
## -0.6792 0.3605 -0.929 0.9833
## -1.0050 -0.0585 -3.406 0.0152
## -0.9599 -0.0272 -3.208 0.0292
## -0.7931 0.1880 -1.869 0.5723
## -0.9548 0.0454 -2.756 0.1064
## 0.0178 0.8640 3.158 0.0341
## -0.0693 0.8334 2.566 0.1686
## -0.3871 0.4063 0.073 1.0000
## -0.3407 0.4363 0.373 1.0000
## -0.1784 0.6561 1.735 0.6640
## -0.3418 0.5151 0.613 0.9987
## -0.5032 0.3855 -0.401 0.9999
## -0.8200 -0.0425 -3.362 0.0176
## -0.7734 -0.0127 -3.132 0.0369
## -0.6117 0.2077 -1.495 0.8107
## -0.7753 0.0668 -2.550 0.1748
## -0.7917 0.0469 -2.692 0.1248
## -0.7458 0.0773 -2.462 0.2121
## -0.5819 0.2956 -0.989 0.9761
## -0.7448 0.1540 -1.992 0.4871
## -0.3126 0.3890 0.330 1.0000
## -0.1531 0.6116 1.817 0.6082
## -0.3175 0.4716 0.592 0.9990
## -0.1828 0.5649 1.549 0.7805
## -0.3475 0.4251 0.305 1.0000
## -0.5674 0.2629 -1.111 0.9546
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 8 estimates
## P value adjustment: tukey method for comparing a family of 8 estimates## Sceloporus aeneus Dry Sceloporus bicanthalis Dry
## Sceloporus aeneus Dry [4.39] 0.0239
## Sceloporus bicanthalis Dry -0.54138 [4.93]
## Sceloporus grammicus Dry -0.10051 0.44087
## Sceloporus spinosus Dry -0.15933 0.38205
## Sceloporus aeneus Rainy -0.53177 0.00961
## Sceloporus bicanthalis Rainy -0.49358 0.04780
## Sceloporus grammicus Rainy -0.30251 0.23887
## Sceloporus spinosus Rainy -0.45474 0.08664
## Sceloporus grammicus Dry Sceloporus spinosus Dry
## Sceloporus aeneus Dry 0.9987 0.9833
## Sceloporus bicanthalis Dry 0.0341 0.1686
## Sceloporus grammicus Dry [4.49] 0.9999
## Sceloporus spinosus Dry -0.05882 [4.55]
## Sceloporus aeneus Rainy -0.43126 -0.37244
## Sceloporus bicanthalis Rainy -0.39307 -0.33425
## Sceloporus grammicus Rainy -0.20200 -0.14318
## Sceloporus spinosus Rainy -0.35423 -0.29541
## Sceloporus aeneus Rainy
## Sceloporus aeneus Dry 0.0152
## Sceloporus bicanthalis Dry 1.0000
## Sceloporus grammicus Dry 0.0176
## Sceloporus spinosus Dry 0.1248
## Sceloporus aeneus Rainy [4.92]
## Sceloporus bicanthalis Rainy 0.03819
## Sceloporus grammicus Rainy 0.22926
## Sceloporus spinosus Rainy 0.07703
## Sceloporus bicanthalis Rainy
## Sceloporus aeneus Dry 0.0292
## Sceloporus bicanthalis Dry 1.0000
## Sceloporus grammicus Dry 0.0369
## Sceloporus spinosus Dry 0.2121
## Sceloporus aeneus Rainy 1.0000
## Sceloporus bicanthalis Rainy [4.89]
## Sceloporus grammicus Rainy 0.19107
## Sceloporus spinosus Rainy 0.03884
## Sceloporus grammicus Rainy
## Sceloporus aeneus Dry 0.5723
## Sceloporus bicanthalis Dry 0.6640
## Sceloporus grammicus Dry 0.8107
## Sceloporus spinosus Dry 0.9761
## Sceloporus aeneus Rainy 0.6082
## Sceloporus bicanthalis Rainy 0.7805
## Sceloporus grammicus Rainy [4.70]
## Sceloporus spinosus Rainy -0.15223
## Sceloporus spinosus Rainy
## Sceloporus aeneus Dry 0.1064
## Sceloporus bicanthalis Dry 0.9987
## Sceloporus grammicus Dry 0.1748
## Sceloporus spinosus Dry 0.4871
## Sceloporus aeneus Rainy 0.9990
## Sceloporus bicanthalis Rainy 1.0000
## Sceloporus grammicus Rainy 0.9546
## Sceloporus spinosus Rainy [4.85]
##
## Row and column labels: SPECIES:SEASON
## Upper triangle: P values adjust = "tukey"
## Diagonal: [Estimates] (emmean)
## Lower triangle: Comparisons (estimate) earlier vs. later
Evaluate whether sex variable influence on gut microbiota diversity (q=2)
# Paired test (Wilcoxon test, q2)
# Sceloporus aeneus
aeneus_fem_GutMic <- subset(Micro_div, Species_Scel == "Sceloporus aeneus" &
Sex == "Female", q2, drop = TRUE)
aeneus_male_GutMic <- subset(Micro_div, Species_Scel == "Sceloporus aeneus" &
Sex == "Male", q2, drop = TRUE)
aeneus_FemMale_GutMic <- wilcox.test(x= aeneus_fem_GutMic, y= aeneus_male_GutMic)
aeneus_FemMale_GutMic##
## Wilcoxon rank sum exact test
##
## data: aeneus_fem_GutMic and aeneus_male_GutMic
## W = 39, p-value = 0.1713
## alternative hypothesis: true location shift is not equal to 0# Sceloporus bicanthalis
bica_fem_GutMic <- subset(Micro_div, Species_Scel == "Sceloporus bicanthalis" &
Sex == "Female", q2, drop = TRUE)
bica_male_GutMic <- subset(Micro_div, Species_Scel == "Sceloporus bicanthalis" &
Sex == "Male", q2, drop = TRUE)
bica_FemMale_GutMic <- wilcox.test(x= bica_fem_GutMic, y= bica_male_GutMic)
bica_FemMale_GutMic##
## Wilcoxon rank sum exact test
##
## data: bica_fem_GutMic and bica_male_GutMic
## W = 69, p-value = 0.9095
## alternative hypothesis: true location shift is not equal to 0# Sceloporus grammicus
gram_fem_GutMic <- subset(Micro_div, Species_Scel == "Sceloporus grammicus" &
Sex == "Female", q2, drop = TRUE)
gram_male_GutMic <- subset(Micro_div, Species_Scel == "Sceloporus grammicus" &
Sex == "Male", q2, drop = TRUE)
gram_FemMale_GutMic <- wilcox.test(x= gram_fem_GutMic, y= gram_male_GutMic)
gram_FemMale_GutMic##
## Wilcoxon rank sum exact test
##
## data: gram_fem_GutMic and gram_male_GutMic
## W = 107, p-value = 0.6313
## alternative hypothesis: true location shift is not equal to 0# Sceloporus spinosus
spi_fem_GutMic <- subset(Micro_div, Species_Scel == "Sceloporus spinosus" &
Sex == "Female", q2, drop = TRUE)
spi_male_GutMic <- subset(Micro_div, Species_Scel == "Sceloporus spinosus" &
Sex == "Male", q2, drop = TRUE)
spi_FemMale_GutMic <- wilcox.test(x= spi_fem_GutMic, y= spi_male_GutMic)
spi_FemMale_GutMic##
## Wilcoxon rank sum exact test
##
## data: spi_fem_GutMic and spi_male_GutMic
## W = 40, p-value = 0.4137
## alternative hypothesis: true location shift is not equal to 0Generalized Linear Model with quasi-Poisson distribution (q = 2)
MODEL1 <- glm(q2 ~ SEASON*SPECIES+ELEVATION+SVL+SEQDEPTH,
family = quasipoisson(link = "log"),
data = Micro_div)
summary(MODEL1)##
## Call:
## glm(formula = q2 ~ SEASON * SPECIES + ELEVATION + SVL + SEQDEPTH,
## family = quasipoisson(link = "log"), data = Micro_div)
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 3.489e+00 3.263e-01 10.694
## SEASONRainy 3.432e-01 2.143e-01 1.601
## SPECIESSceloporus bicanthalis 1.267e+00 6.553e-01 1.933
## SPECIESSceloporus grammicus 6.931e-02 2.358e-01 0.294
## SPECIESSceloporus spinosus 1.270e-01 3.062e-01 0.415
## ELEVATION -4.765e-01 5.739e-01 -0.830
## SVL -2.387e-05 5.754e-03 -0.004
## SEQDEPTH 1.204e-01 1.009e-01 1.193
## SEASONRainy:SPECIESSceloporus bicanthalis -8.202e-01 2.652e-01 -3.093
## SEASONRainy:SPECIESSceloporus grammicus -1.219e-01 2.754e-01 -0.442
## SEASONRainy:SPECIESSceloporus spinosus -3.317e-01 2.909e-01 -1.140
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## SEASONRainy 0.11305
## SPECIESSceloporus bicanthalis 0.05657 .
## SPECIESSceloporus grammicus 0.76956
## SPECIESSceloporus spinosus 0.67944
## ELEVATION 0.40867
## SVL 0.99670
## SEQDEPTH 0.23636
## SEASONRainy:SPECIESSceloporus bicanthalis 0.00269 **
## SEASONRainy:SPECIESSceloporus grammicus 0.65931
## SEASONRainy:SPECIESSceloporus spinosus 0.25746
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 9.481509)
##
## Null deviance: 1039.03 on 94 degrees of freedom
## Residual deviance: 818.79 on 84 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
##
## Call:
## glm(formula = q2 ~ SEASON + SPECIES + ELEVATION + SVL + SEQDEPTH,
## family = quasipoisson(link = "log"), data = Micro_div)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.708501 0.314372 11.797 <2e-16 ***
## SEASONRainy 0.025511 0.109991 0.232 0.817
## SPECIESSceloporus bicanthalis 0.933649 0.676327 1.380 0.171
## SPECIESSceloporus grammicus -0.025499 0.165697 -0.154 0.878
## SPECIESSceloporus spinosus -0.051587 0.267991 -0.192 0.848
## ELEVATION -0.626981 0.606572 -1.034 0.304
## SVL -0.001225 0.005989 -0.205 0.838
## SEQDEPTH 0.041928 0.106778 0.393 0.696
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 10.47637)
##
## Null deviance: 1039.03 on 94 degrees of freedom
## Residual deviance: 934.33 on 87 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = q2 ~ SEASON + SPECIES + ELEVATION + SEQDEPTH, family = quasipoisson(link = "log"),
## data = Micro_div)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.65897 0.19913 18.375 <2e-16 ***
## SEASONRainy 0.02422 0.10921 0.222 0.825
## SPECIESSceloporus bicanthalis 0.92494 0.66933 1.382 0.171
## SPECIESSceloporus grammicus -0.04438 0.13685 -0.324 0.746
## SPECIESSceloporus spinosus -0.09673 0.15179 -0.637 0.526
## ELEVATION -0.61281 0.59747 -1.026 0.308
## SEQDEPTH 0.04319 0.10604 0.407 0.685
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 10.36163)
##
## Null deviance: 1039.03 on 94 degrees of freedom
## Residual deviance: 934.77 on 88 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = q2 ~ SEASON + SPECIES + ELEVATION, family = quasipoisson(link = "log"),
## data = Micro_div)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.64561 0.19533 18.664 <2e-16 ***
## SEASONRainy 0.04488 0.09589 0.468 0.641
## SPECIESSceloporus bicanthalis 0.93231 0.66505 1.402 0.164
## SPECIESSceloporus grammicus -0.04395 0.13615 -0.323 0.748
## SPECIESSceloporus spinosus -0.09462 0.15095 -0.627 0.532
## ELEVATION -0.61383 0.59391 -1.034 0.304
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 10.26101)
##
## Null deviance: 1039.03 on 94 degrees of freedom
## Residual deviance: 936.46 on 89 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = q2 ~ SEASON + SPECIES, family = quasipoisson(link = "log"),
## data = Micro_div)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.80662 0.11637 32.712 <2e-16 ***
## SEASONRainy 0.04940 0.09642 0.512 0.6097
## SPECIESSceloporus bicanthalis 0.25823 0.13175 1.960 0.0531 .
## SPECIESSceloporus grammicus -0.04288 0.13674 -0.314 0.7546
## SPECIESSceloporus spinosus -0.05584 0.14700 -0.380 0.7049
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 10.3626)
##
## Null deviance: 1039.03 on 94 degrees of freedom
## Residual deviance: 950.07 on 90 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4# Best Model
MODEL6 <- glm(q2 ~ SEASON*SPECIES,
family = quasipoisson(link = "log"),
data = Micro_div)
summary(MODEL6)##
## Call:
## glm(formula = q2 ~ SEASON * SPECIES, family = quasipoisson(link = "log"),
## data = Micro_div)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.57869 0.17122 20.902 < 2e-16
## SEASONRainy 0.40334 0.20706 1.948 0.054645
## SPECIESSceloporus bicanthalis 0.74146 0.20804 3.564 0.000596
## SPECIESSceloporus grammicus 0.08107 0.21339 0.380 0.704917
## SPECIESSceloporus spinosus 0.15897 0.22310 0.713 0.478026
## SEASONRainy:SPECIESSceloporus bicanthalis -0.79089 0.26304 -3.007 0.003451
## SEASONRainy:SPECIESSceloporus grammicus -0.14701 0.27125 -0.542 0.589223
## SEASONRainy:SPECIESSceloporus spinosus -0.32729 0.29016 -1.128 0.262430
##
## (Intercept) ***
## SEASONRainy .
## SPECIESSceloporus bicanthalis ***
## SPECIESSceloporus grammicus
## SPECIESSceloporus spinosus
## SEASONRainy:SPECIESSceloporus bicanthalis **
## SEASONRainy:SPECIESSceloporus grammicus
## SEASONRainy:SPECIESSceloporus spinosus
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 9.452614)
##
## Null deviance: 1039.03 on 94 degrees of freedom
## Residual deviance: 840.53 on 87 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4## (Intercept)
## 3.57869413
## SEASONRainy
## 0.40334451
## SPECIESSceloporus bicanthalis
## 0.74145835
## SPECIESSceloporus grammicus
## 0.08107416
## SPECIESSceloporus spinosus
## 0.15897437
## SEASONRainy:SPECIESSceloporus bicanthalis
## -0.79088870
## SEASONRainy:SPECIESSceloporus grammicus
## -0.14700948
## SEASONRainy:SPECIESSceloporus spinosus
## -0.32729337## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) 3.223230360 3.8964819
## SEASONRainy 0.006183591 0.8207875
## SPECIESSceloporus bicanthalis 0.342095601 1.1605800
## SPECIESSceloporus grammicus -0.330307974 0.5093207
## SPECIESSceloporus spinosus -0.274369614 0.6037278
## SEASONRainy:SPECIESSceloporus bicanthalis -1.313319633 -0.2802605
## SEASONRainy:SPECIESSceloporus grammicus -0.685102597 0.3801804
## SEASONRainy:SPECIESSceloporus spinosus -0.902353379 0.2372010Residual plot of the best model
Plot model assumption
residuals_q2 <- plot_model(MODEL6, type = "eff", terms = c("SEASON","SPECIES"),
colors = c("#999999", "#E69F00", "#56B4E9",
"#009E73", "#F0E442")) +
theme_classic() +
theme(legend.text = element_text(face = "italic")) +
theme(axis.text.x = element_text(size = 11))
residuals_q2 
Contrasts (dry vs rainy), CI95%, p values
## SPECIES SEASON rate SE df asymp.LCL asymp.UCL
## Sceloporus aeneus Dry 35.8 6.13 Inf 25.6 50.1
## Sceloporus bicanthalis Dry 75.2 8.89 Inf 59.7 94.8
## Sceloporus grammicus Dry 38.9 4.95 Inf 30.3 49.9
## Sceloporus spinosus Dry 42.0 6.01 Inf 31.7 55.6
## Sceloporus aeneus Rainy 53.6 6.24 Inf 42.7 67.4
## Sceloporus bicanthalis Rainy 51.0 5.67 Inf 41.1 63.5
## Sceloporus grammicus Rainy 50.2 6.04 Inf 39.7 63.6
## Sceloporus spinosus Rainy 45.3 6.55 Inf 34.1 60.1
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log scaleM1.emm.s_q2 <- emmeans(MODEL6, specs = ~ SPECIES*SEASON)
pairs(M1.emm.s_q2, adjust = "tukey", infer=c(TRUE,TRUE))## contrast estimate SE df
## Sceloporus aeneus Dry - Sceloporus bicanthalis Dry -0.7415 0.208 Inf
## Sceloporus aeneus Dry - Sceloporus grammicus Dry -0.0811 0.213 Inf
## Sceloporus aeneus Dry - Sceloporus spinosus Dry -0.1590 0.223 Inf
## Sceloporus aeneus Dry - Sceloporus aeneus Rainy -0.4033 0.207 Inf
## Sceloporus aeneus Dry - Sceloporus bicanthalis Rainy -0.3539 0.204 Inf
## Sceloporus aeneus Dry - Sceloporus grammicus Rainy -0.3374 0.209 Inf
## Sceloporus aeneus Dry - Sceloporus spinosus Rainy -0.2350 0.224 Inf
## Sceloporus bicanthalis Dry - Sceloporus grammicus Dry 0.6604 0.174 Inf
## Sceloporus bicanthalis Dry - Sceloporus spinosus Dry 0.5825 0.186 Inf
## Sceloporus bicanthalis Dry - Sceloporus aeneus Rainy 0.3381 0.166 Inf
## Sceloporus bicanthalis Dry - Sceloporus bicanthalis Rainy 0.3875 0.162 Inf
## Sceloporus bicanthalis Dry - Sceloporus grammicus Rainy 0.4040 0.169 Inf
## Sceloporus bicanthalis Dry - Sceloporus spinosus Rainy 0.5064 0.187 Inf
## Sceloporus grammicus Dry - Sceloporus spinosus Dry -0.0779 0.192 Inf
## Sceloporus grammicus Dry - Sceloporus aeneus Rainy -0.3223 0.173 Inf
## Sceloporus grammicus Dry - Sceloporus bicanthalis Rainy -0.2728 0.169 Inf
## Sceloporus grammicus Dry - Sceloporus grammicus Rainy -0.2563 0.175 Inf
## Sceloporus grammicus Dry - Sceloporus spinosus Rainy -0.1540 0.193 Inf
## Sceloporus spinosus Dry - Sceloporus aeneus Rainy -0.2444 0.184 Inf
## Sceloporus spinosus Dry - Sceloporus bicanthalis Rainy -0.1949 0.181 Inf
## Sceloporus spinosus Dry - Sceloporus grammicus Rainy -0.1784 0.187 Inf
## Sceloporus spinosus Dry - Sceloporus spinosus Rainy -0.0761 0.203 Inf
## Sceloporus aeneus Rainy - Sceloporus bicanthalis Rainy 0.0494 0.161 Inf
## Sceloporus aeneus Rainy - Sceloporus grammicus Rainy 0.0659 0.167 Inf
## Sceloporus aeneus Rainy - Sceloporus spinosus Rainy 0.1683 0.186 Inf
## Sceloporus bicanthalis Rainy - Sceloporus grammicus Rainy 0.0165 0.164 Inf
## Sceloporus bicanthalis Rainy - Sceloporus spinosus Rainy 0.1189 0.182 Inf
## Sceloporus grammicus Rainy - Sceloporus spinosus Rainy 0.1024 0.188 Inf
## asymp.LCL asymp.UCL z.ratio p.value
## -1.3720 -0.111 -3.564 0.0087
## -0.7278 0.566 -0.380 0.9999
## -0.8352 0.517 -0.713 0.9967
## -1.0309 0.224 -1.948 0.5176
## -0.9726 0.265 -1.734 0.6649
## -0.9717 0.297 -1.612 0.7431
## -0.9139 0.444 -1.049 0.9668
## 0.1338 1.187 3.801 0.0036
## 0.0201 1.145 3.139 0.0361
## -0.1647 0.841 2.038 0.4560
## -0.1041 0.879 2.389 0.2465
## -0.1072 0.915 2.396 0.2433
## -0.0592 1.072 2.714 0.1183
## -0.6584 0.503 -0.407 0.9999
## -0.8453 0.201 -1.868 0.5734
## -0.7851 0.239 -1.614 0.7418
## -0.7874 0.275 -1.463 0.8272
## -0.7376 0.430 -0.800 0.9932
## -0.8034 0.315 -1.325 0.8897
## -0.7439 0.354 -1.076 0.9618
## -0.7450 0.388 -0.955 0.9805
## -0.6921 0.540 -0.374 1.0000
## -0.4384 0.537 0.307 1.0000
## -0.4416 0.573 0.394 0.9999
## -0.3940 0.731 0.907 0.9855
## -0.4799 0.513 0.101 1.0000
## -0.4334 0.671 0.652 0.9981
## -0.4674 0.672 0.545 0.9994
##
## Results are given on the log (not the response) scale.
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 8 estimates
## P value adjustment: tukey method for comparing a family of 8 estimates## contrast estimate SE df
## Sceloporus aeneus Dry - Sceloporus bicanthalis Dry -0.7415 0.208 Inf
## Sceloporus aeneus Dry - Sceloporus grammicus Dry -0.0811 0.213 Inf
## Sceloporus aeneus Dry - Sceloporus spinosus Dry -0.1590 0.223 Inf
## Sceloporus aeneus Dry - Sceloporus aeneus Rainy -0.4033 0.207 Inf
## Sceloporus aeneus Dry - Sceloporus bicanthalis Rainy -0.3539 0.204 Inf
## Sceloporus aeneus Dry - Sceloporus grammicus Rainy -0.3374 0.209 Inf
## Sceloporus aeneus Dry - Sceloporus spinosus Rainy -0.2350 0.224 Inf
## Sceloporus bicanthalis Dry - Sceloporus grammicus Dry 0.6604 0.174 Inf
## Sceloporus bicanthalis Dry - Sceloporus spinosus Dry 0.5825 0.186 Inf
## Sceloporus bicanthalis Dry - Sceloporus aeneus Rainy 0.3381 0.166 Inf
## Sceloporus bicanthalis Dry - Sceloporus bicanthalis Rainy 0.3875 0.162 Inf
## Sceloporus bicanthalis Dry - Sceloporus grammicus Rainy 0.4040 0.169 Inf
## Sceloporus bicanthalis Dry - Sceloporus spinosus Rainy 0.5064 0.187 Inf
## Sceloporus grammicus Dry - Sceloporus spinosus Dry -0.0779 0.192 Inf
## Sceloporus grammicus Dry - Sceloporus aeneus Rainy -0.3223 0.173 Inf
## Sceloporus grammicus Dry - Sceloporus bicanthalis Rainy -0.2728 0.169 Inf
## Sceloporus grammicus Dry - Sceloporus grammicus Rainy -0.2563 0.175 Inf
## Sceloporus grammicus Dry - Sceloporus spinosus Rainy -0.1540 0.193 Inf
## Sceloporus spinosus Dry - Sceloporus aeneus Rainy -0.2444 0.184 Inf
## Sceloporus spinosus Dry - Sceloporus bicanthalis Rainy -0.1949 0.181 Inf
## Sceloporus spinosus Dry - Sceloporus grammicus Rainy -0.1784 0.187 Inf
## Sceloporus spinosus Dry - Sceloporus spinosus Rainy -0.0761 0.203 Inf
## Sceloporus aeneus Rainy - Sceloporus bicanthalis Rainy 0.0494 0.161 Inf
## Sceloporus aeneus Rainy - Sceloporus grammicus Rainy 0.0659 0.167 Inf
## Sceloporus aeneus Rainy - Sceloporus spinosus Rainy 0.1683 0.186 Inf
## Sceloporus bicanthalis Rainy - Sceloporus grammicus Rainy 0.0165 0.164 Inf
## Sceloporus bicanthalis Rainy - Sceloporus spinosus Rainy 0.1189 0.182 Inf
## Sceloporus grammicus Rainy - Sceloporus spinosus Rainy 0.1024 0.188 Inf
## z.ratio p.value
## -3.564 0.0087
## -0.380 0.9999
## -0.713 0.9967
## -1.948 0.5176
## -1.734 0.6649
## -1.612 0.7431
## -1.049 0.9668
## 3.801 0.0036
## 3.139 0.0361
## 2.038 0.4560
## 2.389 0.2465
## 2.396 0.2433
## 2.714 0.1183
## -0.407 0.9999
## -1.868 0.5734
## -1.614 0.7418
## -1.463 0.8272
## -0.800 0.9932
## -1.325 0.8897
## -1.076 0.9618
## -0.955 0.9805
## -0.374 1.0000
## 0.307 1.0000
## 0.394 0.9999
## 0.907 0.9855
## 0.101 1.0000
## 0.652 0.9981
## 0.545 0.9994
##
## Results are given on the log (not the response) scale.
## P value adjustment: tukey method for comparing a family of 8 estimates## Sceloporus aeneus Dry Sceloporus bicanthalis Dry
## Sceloporus aeneus Dry [3.58] 0.0087
## Sceloporus bicanthalis Dry -0.7415 [4.32]
## Sceloporus grammicus Dry -0.0811 0.6604
## Sceloporus spinosus Dry -0.1590 0.5825
## Sceloporus aeneus Rainy -0.4033 0.3381
## Sceloporus bicanthalis Rainy -0.3539 0.3875
## Sceloporus grammicus Rainy -0.3374 0.4040
## Sceloporus spinosus Rainy -0.2350 0.5064
## Sceloporus grammicus Dry Sceloporus spinosus Dry
## Sceloporus aeneus Dry 0.9999 0.9967
## Sceloporus bicanthalis Dry 0.0036 0.0361
## Sceloporus grammicus Dry [3.66] 0.9999
## Sceloporus spinosus Dry -0.0779 [3.74]
## Sceloporus aeneus Rainy -0.3223 -0.2444
## Sceloporus bicanthalis Rainy -0.2728 -0.1949
## Sceloporus grammicus Rainy -0.2563 -0.1784
## Sceloporus spinosus Rainy -0.1540 -0.0761
## Sceloporus aeneus Rainy
## Sceloporus aeneus Dry 0.5176
## Sceloporus bicanthalis Dry 0.4560
## Sceloporus grammicus Dry 0.5734
## Sceloporus spinosus Dry 0.8897
## Sceloporus aeneus Rainy [3.98]
## Sceloporus bicanthalis Rainy 0.0494
## Sceloporus grammicus Rainy 0.0659
## Sceloporus spinosus Rainy 0.1683
## Sceloporus bicanthalis Rainy
## Sceloporus aeneus Dry 0.6649
## Sceloporus bicanthalis Dry 0.2465
## Sceloporus grammicus Dry 0.7418
## Sceloporus spinosus Dry 0.9618
## Sceloporus aeneus Rainy 1.0000
## Sceloporus bicanthalis Rainy [3.93]
## Sceloporus grammicus Rainy 0.0165
## Sceloporus spinosus Rainy 0.1189
## Sceloporus grammicus Rainy
## Sceloporus aeneus Dry 0.7431
## Sceloporus bicanthalis Dry 0.2433
## Sceloporus grammicus Dry 0.8272
## Sceloporus spinosus Dry 0.9805
## Sceloporus aeneus Rainy 0.9999
## Sceloporus bicanthalis Rainy 1.0000
## Sceloporus grammicus Rainy [3.92]
## Sceloporus spinosus Rainy 0.1024
## Sceloporus spinosus Rainy
## Sceloporus aeneus Dry 0.9668
## Sceloporus bicanthalis Dry 0.1183
## Sceloporus grammicus Dry 0.9932
## Sceloporus spinosus Dry 1.0000
## Sceloporus aeneus Rainy 0.9855
## Sceloporus bicanthalis Rainy 0.9981
## Sceloporus grammicus Rainy 0.9994
## Sceloporus spinosus Rainy [3.81]
##
## Row and column labels: SPECIES:SEASON
## Upper triangle: P values adjust = "tukey"
## Diagonal: [Estimates] (emmean)
## Lower triangle: Comparisons (estimate) earlier vs. later
Relation between alpha taxonomic microbiota and diet
Micro_div$Ind <- substr(Micro_div$SampleID, 8, nchar(Micro_div$SampleID) - 0)
diet_phylo_div <- read.csv("../data/diet_Hill_numbers_q012.csv", header = TRUE)
# Generar una nueva columna con la información de otra columna, excluyendo los últimos 4 caracteres
diet_phylo_div$Ind <- substr(diet_phylo_div$SampleID, 8, nchar(diet_phylo_div$SampleID) - 4)
#names(diet_phylo_div)[names(diet_phylo_div) %in% c("q0", "q1", "q2")] <- c("d.p.0", "d.p.1", #"d.p.2")
divs <- Micro_div %>%
inner_join(diet_phylo_div, by = c("Ind"="Ind"))
#write.table(divs, file="../data/full_divs.txt", sep = "\t")
facet_label_italic <- function(variable, value) {
return(paste0(value, ": Species_Scel"))
}
##Plot
taxq1_tq0 <- ggscatter(divs, x = "tq0", y = "q1", color = "Species_Scel",
palette = c("#999999", "#E69F00", "#56B4E9", "#009E73"), xlab= "Taxonomic richness of diet (number of taxonomic groups)", ylab="Gut bacterial diversity (effective number of frequent ASVs)",
add = "reg.line", # Add regressin line
add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
conf.int = TRUE, # Add confidence interval
cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
cor.coeff.args = list(method = "pearson", label.x = 3, label.sep = "\n")
)+
theme(legend.title = element_blank(), legend.position = "none")
taxq2_tq0 <- ggscatter(divs, x = "tq0", y = "q2", color = "Species_Scel",
palette = c("#999999", "#E69F00", "#56B4E9", "#009E73"),
xlab= "Taxonomic richness of diet (number of taxonomic groups)", ylab="Gut bacterial diversity (effective number of dominant ASVs)",
add = "reg.line", # Add regressin line
add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
conf.int = TRUE, # Add confidence interval
cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
cor.coeff.args = list(method = "pearson", label.x = 3, label.sep = "\n")
)+
theme(legend.title = element_blank(), legend.position = "none")
phyq1_pq0 <- ggscatter(divs, x = "pq0", y = "q1", color = "Species_Scel",
palette = c("#999999", "#E69F00", "#56B4E9", "#009E73") ,
xlab= " Phylogenetic richness of diet (number of phylogenetic lineages)", ylab="Gut bacterial diversity (effective number of frequent ASVs)",
add = "reg.line", # Add regressin line
add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
conf.int = TRUE, # Add confidence interval
cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
cor.coeff.args = list(method = "pearson", label.x = 3, label.sep = "\n")
)+
theme(legend.title = element_blank(), legend.position = "none")
phyq2_pq0 <- ggscatter(divs, x = "pq0", y = "q2", color = "Species_Scel",
palette = c("#999999", "#E69F00", "#56B4E9", "#009E73"),
xlab= "Phylogenetic richness of diet (number of phylogenetic lineages)",
ylab="Gut bacterial diversity (effective number of dominant ASVs)",
add = "reg.line", # Add regressin line
add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
conf.int = TRUE, # Add confidence interval
cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
cor.coeff.args = list(method = "pearson", label.x = 3, label.sep = "\n")
)+
theme(legend.title = element_blank(), legend.position = "none")
taxq1_tq0.sp <- ggscatter(divs, x = "tq0", y = "q1", color = "Species_Scel",
palette = c("#999999", "#E69F00", "#56B4E9", "#009E73"),
xlab= "Taxonomic richness of diet (number of taxonomic groups)",
ylab="Gut bacterial diversity (effective number of frequent ASVs)",
facet.by = "Species_Scel",
add = "reg.line", # Add regressin line
add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
conf.int = TRUE, # Add confidence interval
cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
cor.coeff.args = list(method = "pearson",
label.x = 3, label.sep = "\n")
)+
theme(legend.position = "none",
strip.text = element_text(face = "italic"))
taxq2_tq0.sp <- ggscatter(divs, x = "tq0", y = "q2", color = "Species_Scel",
palette = c("#999999", "#E69F00", "#56B4E9", "#009E73"),
xlab= "Taxonomic richness of diet (number of taxonomic groups)", ylab="Gut bacterial diversity (effective number of dominant ASVs)",
facet.by = "Species_Scel" ,
add = "reg.line", # Add regressin line
add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
conf.int = TRUE, # Add confidence interval
cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
cor.coeff.args = list(method = "pearson", label.x = 3, label.sep = "\n")
)+
theme(legend.position = "none",
strip.text = element_text(face = "italic"))
phyq1_pq0.sp <- ggscatter(divs, x = "pq0", y = "q1", color = "Species_Scel",
palette = c("#999999", "#E69F00", "#56B4E9", "#009E73") ,
xlab= " Phylogenetic richness of diet (number of phylogenetic lineages)", ylab="Gut bacterial diversity (effective number of frequent ASVs)",
facet.by = "Species_Scel",
add = "reg.line", # Add regressin line
add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
conf.int = TRUE, # Add confidence interval
cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
cor.coeff.args = list(method = "pearson", label.x = 3, label.sep = "\n")
)+
theme(legend.position = "none",
strip.text = element_text(face = "italic"))
phyq2_pq0.sp <- ggscatter(divs, x = "pq0", y = "q2", color = "Species_Scel",
palette = c("#999999", "#E69F00", "#56B4E9", "#009E73"),
xlab= "Phylogenetic richness of diet (number of phylogenetic lineages)", ylab="Gut bacterial diversity (effective number of dominant ASVs)",
facet.by = "Species_Scel" ,
add = "reg.line", # Add regressin line
add.params = list(color = "blue", fill = "lightgray"), # Customize reg. line
conf.int = TRUE, # Add confidence interval
cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
cor.coeff.args = list(method = "pearson", label.x = 3, label.sep = "\n")
)+
theme(legend.position = "none",
strip.text = element_text(face = "italic"))
taxq1_tq0
diet.vs.micro1<- plot_grid(taxq1_tq0, taxq1_tq0.sp,
labels = "AUTO", ncol = 1)
ggsave("../figures/dietvsmicro1.jpeg", width=8, height=11, dpi=300)
diet.vs.micro2<- plot_grid(taxq2_tq0, taxq2_tq0.sp,
labels = c("E","F"), ncol = 1)
ggsave("../figures/dietvsmicro2.jpeg", width=8, height=11, dpi=300)
diet.vs.micro3<- plot_grid(phyq1_pq0, phyq1_pq0.sp,
labels = c("C", "D"), ncol = 1)
ggsave("../figures/dietvsmicro3.jpeg", width=8, height=11, dpi=300)
diet.vs.micro4<- plot_grid(phyq2_pq0, phyq2_pq0.sp,
labels = c("G", "H"), ncol = 1)
ggsave("../figures/dietvsmicro4.jpeg", width=8, height=11, dpi=300)Effect of diet on gut microbiota
loading files
Check distribution of the dependent variable (diversity q=0, Hill numbers)
##
## Shapiro-Wilk normality test
##
## data: Microb_data$q0
## W = 0.92649, p-value = 0.0009384##
## Shapiro-Wilk normality test
##
## data: Microb_data$q1
## W = 0.97555, p-value = 0.2334
##
## Bartlett test of homogeneity of variances
##
## data: q0 by Species_Scel
## Bartlett's K-squared = 9.0931, df = 3, p-value = 0.02808## Warning in leveneTest.default(y = y, group = group, ...): group coerced to
## factor.Plot variances among lizard species (q0)
boxplot(q0 ~ Species_Scel, data = Microb_data,
main = "Differences in variance among lizard species",
xlab = "Species",
ylab = " Hill numbers (q0)",
col = "#4682B433",
border = "black")
###Preparing the data
#Declare as factors
SPECIES <- as.factor(Microb_data$Species_Scel) # Four Sceloporus lizard species
SEASON <- as.factor(Microb_data$Season) # Dry and Rainy
SEX <- as.factor(Microb_data$Sex) # Male and Female
#Standardize continous independent variables
SVL <- rescale(Microb_data$SVL, binary.inputs = "center") # Snout–vent length measured in mm.
ELEVATION <- rescale(Microb_data$Elevation, binary.inputs = "center") # Taken as m a.s.l.
SEQDEPTH <- rescale(Microb_data$SeqDepth, binary.inputs = "center")Linear model with Poisson distribution (taxonomic q = 0)
With Microbial Diversity (q1)
## UPDATE FUNCTION (RUN ANOVA)
# 68 samples (diet / microbiota)
m1 <- glm(q1 ~ Diet*SPECIES+SEASON,
family = quasipoisson(link = "log"),
data = Microb_data)
summary(m1)##
## Call:
## glm(formula = q1 ~ Diet * SPECIES + SEASON, family = quasipoisson(link = "log"),
## data = Microb_data)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.6562151 0.1865713 24.957 < 2e-16 ***
## Diet -0.0004683 0.0196703 -0.024 0.98109
## SPECIESSceloporus bicanthalis 0.0595147 0.2216353 0.269 0.78930
## SPECIESSceloporus grammicus -0.1948473 0.2453897 -0.794 0.43059
## SPECIESSceloporus spinosus 0.1751032 0.2349154 0.745 0.45921
## SEASONRainy 0.2689010 0.0857259 3.137 0.00274 **
## Diet:SPECIESSceloporus bicanthalis 0.0018106 0.0258806 0.070 0.94448
## Diet:SPECIESSceloporus grammicus -0.0084125 0.0312962 -0.269 0.78909
## Diet:SPECIESSceloporus spinosus -0.0373851 0.0318303 -1.175 0.24525
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 10.57198)
##
## Null deviance: 932.15 on 63 degrees of freedom
## Residual deviance: 593.29 on 55 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
##
## Call:
## glm(formula = q1 ~ Diet + SPECIES + SEASON, family = quasipoisson(link = "log"),
## data = Microb_data)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.704855 0.130358 36.092 < 2e-16 ***
## Diet -0.007877 0.010173 -0.774 0.44187
## SPECIESSceloporus bicanthalis 0.061506 0.106646 0.577 0.56636
## SPECIESSceloporus grammicus -0.256866 0.111237 -2.309 0.02452 *
## SPECIESSceloporus spinosus -0.047488 0.116892 -0.406 0.68605
## SEASONRainy 0.283586 0.080238 3.534 0.00081 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 10.42645)
##
## Null deviance: 932.15 on 63 degrees of freedom
## Residual deviance: 614.21 on 58 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = q1 ~ SPECIES + SEASON, family = quasipoisson(link = "log"),
## data = Microb_data)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.63657 0.09649 48.053 < 2e-16 ***
## SPECIESSceloporus bicanthalis 0.07079 0.10567 0.670 0.505539
## SPECIESSceloporus grammicus -0.24165 0.10918 -2.213 0.030761 *
## SPECIESSceloporus spinosus -0.02901 0.11425 -0.254 0.800452
## SEASONRainy 0.29232 0.07918 3.692 0.000488 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 10.37193)
##
## Null deviance: 932.15 on 63 degrees of freedom
## Residual deviance: 620.50 on 59 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = q1 ~ SEASON, family = quasipoisson(link = "log"),
## data = Microb_data)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.55181 0.06272 72.574 < 2e-16 ***
## SEASONRainy 0.33820 0.08005 4.225 7.99e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 11.18809)
##
## Null deviance: 932.15 on 63 degrees of freedom
## Residual deviance: 728.20 on 62 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = q1 ~ SPECIES, family = quasipoisson(link = "log"),
## data = Microb_data)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.82632 0.08770 55.033 <2e-16 ***
## SPECIESSceloporus bicanthalis 0.09084 0.11575 0.785 0.4356
## SPECIESSceloporus grammicus -0.29554 0.11870 -2.490 0.0156 *
## SPECIESSceloporus spinosus -0.08290 0.12430 -0.667 0.5074
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 12.47322)
##
## Null deviance: 932.15 on 63 degrees of freedom
## Residual deviance: 764.28 on 60 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4# Plot model assumption
plot_model(m3, type = "eff", terms = "SPECIES",
colors = c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442")) +
theme(legend.text = element_text(face = "italic"))
plot_model(m3, type = "eff", terms = c("SPECIES", "SEASON"),
colors = c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442")) +
theme_classic() +
theme(legend.text = element_text(face = "italic")) +
theme(axis.text.x = element_text(size = 11))
With Microbial Diversity (q2)
model1 <- glm(q2 ~ Diet*SPECIES+SEASON,
family = quasipoisson(link = "log"),
data = Microb_data)
summary(model1)##
## Call:
## glm(formula = q2 ~ Diet * SPECIES + SEASON, family = quasipoisson(link = "log"),
## data = Microb_data)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.62441 0.25085 14.449 <2e-16 ***
## Diet 0.01764 0.02568 0.687 0.4950
## SPECIESSceloporus bicanthalis 0.34411 0.29428 1.169 0.2473
## SPECIESSceloporus grammicus 0.08006 0.32076 0.250 0.8038
## SPECIESSceloporus spinosus 0.45688 0.31449 1.453 0.1520
## SEASONRainy 0.11588 0.10904 1.063 0.2925
## Diet:SPECIESSceloporus bicanthalis -0.01057 0.03311 -0.319 0.7507
## Diet:SPECIESSceloporus grammicus -0.03106 0.04019 -0.773 0.4429
## Diet:SPECIESSceloporus spinosus -0.07581 0.04204 -1.803 0.0768 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 7.255196)
##
## Null deviance: 528.4 on 63 degrees of freedom
## Residual deviance: 401.7 on 55 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
##
## Call:
## glm(formula = q2 ~ Diet + SPECIES + SEASON, family = quasipoisson(link = "log"),
## data = Microb_data)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.79639 0.17233 22.030 <2e-16 ***
## Diet -0.00507 0.01324 -0.383 0.7030
## SPECIESSceloporus bicanthalis 0.24216 0.14164 1.710 0.0927 .
## SPECIESSceloporus grammicus -0.15074 0.14887 -1.013 0.3155
## SPECIESSceloporus spinosus -0.02013 0.15835 -0.127 0.8993
## SEASONRainy 0.13451 0.10404 1.293 0.2012
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 7.364622)
##
## Null deviance: 528.40 on 63 degrees of freedom
## Residual deviance: 430.43 on 58 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = q2 ~ SPECIES + SEASON, family = quasipoisson(link = "log"),
## data = Microb_data)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.752299 0.127845 29.350 <2e-16 ***
## SPECIESSceloporus bicanthalis 0.247615 0.139789 1.771 0.0817 .
## SPECIESSceloporus grammicus -0.140309 0.145194 -0.966 0.3378
## SPECIESSceloporus spinosus -0.008757 0.154400 -0.057 0.9550
## SEASONRainy 0.140365 0.102034 1.376 0.1741
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 7.248433)
##
## Null deviance: 528.40 on 63 degrees of freedom
## Residual deviance: 431.52 on 59 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = q2 ~ SPECIES, family = quasipoisson(link = "log"),
## data = Microb_data)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.84098 0.10960 35.046 <2e-16 ***
## SPECIESSceloporus bicanthalis 0.25751 0.13984 1.841 0.0705 .
## SPECIESSceloporus grammicus -0.16641 0.14420 -1.154 0.2531
## SPECIESSceloporus spinosus -0.03486 0.15350 -0.227 0.8211
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 7.272215)
##
## Null deviance: 528.40 on 63 degrees of freedom
## Residual deviance: 445.32 on 60 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = q2 ~ 1, family = quasipoisson(link = "log"), data = Microb_data)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.8565 0.0515 74.88 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 8.029905)
##
## Null deviance: 528.4 on 63 degrees of freedom
## Residual deviance: 528.4 on 63 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4# Plot model assumption
plot_model(model3, type = "eff", terms = "SPECIES",
colors = c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442")) +
theme(legend.text = element_text(face = "italic"))
Linear models with Poisson distribution (Phylogenetic q = 0)
With Microbial Diversity (q1)
divs$phyDiet<- divs$pq0
#Declare as factors
SPECIES <- as.factor(divs$Species_Scel) # Four Sceloporus lizard species
SEASON <- as.factor(divs$Season) # Dry and Rainy
SEX <- as.factor(divs$Sex) # Male and Female
#Standardize continous independent variables
SVL <- rescale(divs$SVL, binary.inputs = "center") # Snout–vent length measured in mm.
ELEVATION <- rescale(divs$Elevation, binary.inputs = "center") # Taken as m a.s.l.
SEQDEPTH <- rescale(divs$SeqDepth, binary.inputs = "center")
## UPDATE FUNCTION (RUN ANOVA)
# 68 samples (diet / microbiota)
m1 <- glm(q1 ~ phyDiet*SPECIES+SEASON,
family = quasipoisson(link = "log"),
data = divs)
summary(m1)##
## Call:
## glm(formula = q1 ~ phyDiet * SPECIES + SEASON, family = quasipoisson(link = "log"),
## data = divs)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.661408 0.206567 22.566 < 2e-16 ***
## phyDiet -0.005446 0.069609 -0.078 0.93791
## SPECIESSceloporus bicanthalis -0.042498 0.236501 -0.180 0.85803
## SPECIESSceloporus grammicus -0.262152 0.243544 -1.076 0.28628
## SPECIESSceloporus spinosus -0.013347 0.247408 -0.054 0.95717
## SEASONRainy 0.276381 0.088922 3.108 0.00294 **
## phyDiet:SPECIESSceloporus bicanthalis 0.050503 0.084691 0.596 0.55332
## phyDiet:SPECIESSceloporus grammicus -0.004840 0.086901 -0.056 0.95577
## phyDiet:SPECIESSceloporus spinosus -0.014879 0.082267 -0.181 0.85711
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 10.85698)
##
## Null deviance: 944.88 on 65 degrees of freedom
## Residual deviance: 619.92 on 57 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
##
## Call:
## glm(formula = q1 ~ phyDiet + SPECIES + SEASON, family = quasipoisson(link = "log"),
## data = divs)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.651537 0.133638 34.807 < 2e-16 ***
## phyDiet 0.001562 0.026733 0.058 0.95360
## SPECIESSceloporus bicanthalis 0.073228 0.106615 0.687 0.49483
## SPECIESSceloporus grammicus -0.274588 0.113016 -2.430 0.01812 *
## SPECIESSceloporus spinosus -0.062852 0.109585 -0.574 0.56842
## SEASONRainy 0.264348 0.086185 3.067 0.00324 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 10.49499)
##
## Null deviance: 944.88 on 65 degrees of freedom
## Residual deviance: 633.01 on 60 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = q1 ~ SPECIES + SEASON, family = quasipoisson(link = "log"),
## data = divs)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.65694 0.09566 48.684 < 2e-16 ***
## SPECIESSceloporus bicanthalis 0.07275 0.10543 0.690 0.49280
## SPECIESSceloporus grammicus -0.27534 0.11136 -2.472 0.01622 *
## SPECIESSceloporus spinosus -0.06223 0.10818 -0.575 0.56720
## SEASONRainy 0.26231 0.07814 3.357 0.00136 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 10.32373)
##
## Null deviance: 944.88 on 65 degrees of freedom
## Residual deviance: 633.05 on 61 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = q1 ~ SEASON, family = quasipoisson(link = "log"),
## data = divs)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.55181 0.06312 72.115 < 2e-16 ***
## SEASONRainy 0.32085 0.07995 4.013 0.00016 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 11.33081)
##
## Null deviance: 944.88 on 65 degrees of freedom
## Residual deviance: 758.28 on 64 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = q1 ~ SPECIES, family = quasipoisson(link = "log"),
## data = divs)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.82632 0.08573 56.296 < 2e-16 ***
## SPECIESSceloporus bicanthalis 0.09084 0.11315 0.803 0.42514
## SPECIESSceloporus grammicus -0.33142 0.11834 -2.801 0.00679 **
## SPECIESSceloporus spinosus -0.08424 0.11604 -0.726 0.47057
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 11.91967)
##
## Null deviance: 944.88 on 65 degrees of freedom
## Residual deviance: 751.44 on 62 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4# Plot model assumption
plot_model(m3, type = "eff", terms = "SPECIES",
colors = c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442")) +
theme(legend.text = element_text(face = "italic"))
plot_model(m3, type = "eff", terms = c("SPECIES", "SEASON"),
colors = c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442")) +
theme_classic() +
theme(legend.text = element_text(face = "italic")) +
theme(axis.text.x = element_text(size = 11))
With Microbial Diversity (q2)
model1 <- glm(q2 ~ phyDiet*SPECIES+SEASON,
family = quasipoisson(link = "log"),
data = divs)
summary(model1)##
## Call:
## glm(formula = q2 ~ phyDiet * SPECIES + SEASON, family = quasipoisson(link = "log"),
## data = divs)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.878108 0.280118 13.845 <2e-16 ***
## phyDiet -0.042399 0.096811 -0.438 0.663
## SPECIESSceloporus bicanthalis 0.005418 0.319419 0.017 0.987
## SPECIESSceloporus grammicus -0.258721 0.327267 -0.791 0.432
## SPECIESSceloporus spinosus 0.045784 0.335106 0.137 0.892
## SEASONRainy 0.112807 0.115877 0.974 0.334
## phyDiet:SPECIESSceloporus bicanthalis 0.101063 0.114513 0.883 0.381
## phyDiet:SPECIESSceloporus grammicus 0.034825 0.117544 0.296 0.768
## phyDiet:SPECIESSceloporus spinosus -0.026253 0.113567 -0.231 0.818
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 7.702367)
##
## Null deviance: 568.65 on 65 degrees of freedom
## Residual deviance: 447.64 on 57 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4
##
## Call:
## glm(formula = q2 ~ phyDiet + SPECIES + SEASON, family = quasipoisson(link = "log"),
## data = divs)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.82070 0.17848 21.407 <2e-16 ***
## phyDiet -0.01355 0.03571 -0.379 0.7058
## SPECIESSceloporus bicanthalis 0.24687 0.14407 1.714 0.0918 .
## SPECIESSceloporus grammicus -0.17655 0.15307 -1.153 0.2533
## SPECIESSceloporus spinosus -0.04872 0.15269 -0.319 0.7508
## SEASONRainy 0.08802 0.11442 0.769 0.4448
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 7.670608)
##
## Null deviance: 568.65 on 65 degrees of freedom
## Residual deviance: 467.11 on 60 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = q2 ~ SPECIES + SEASON, family = quasipoisson(link = "log"),
## data = divs)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.77434 0.12992 29.052 <2e-16 ***
## SPECIESSceloporus bicanthalis 0.24998 0.14286 1.750 0.0852 .
## SPECIESSceloporus grammicus -0.17020 0.15117 -1.126 0.2646
## SPECIESSceloporus spinosus -0.05479 0.15090 -0.363 0.7178
## SEASONRainy 0.10615 0.10332 1.027 0.3083
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 7.57)
##
## Null deviance: 568.65 on 65 degrees of freedom
## Residual deviance: 468.22 on 61 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = q2 ~ SPECIES, family = quasipoisson(link = "log"),
## data = divs)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.84098 0.11157 34.427 <2e-16 ***
## SPECIESSceloporus bicanthalis 0.25751 0.14236 1.809 0.0753 .
## SPECIESSceloporus grammicus -0.19303 0.14920 -1.294 0.2006
## SPECIESSceloporus spinosus -0.06383 0.15031 -0.425 0.6725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 7.536168)
##
## Null deviance: 568.65 on 65 degrees of freedom
## Residual deviance: 476.25 on 62 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4##
## Call:
## glm(formula = q2 ~ 1, family = quasipoisson(link = "log"), data = divs)
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.84306 0.05214 73.7 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 8.3741)
##
## Null deviance: 568.65 on 65 degrees of freedom
## Residual deviance: 568.65 on 65 degrees of freedom
## AIC: NA
##
## Number of Fisher Scoring iterations: 4# Plot model assumption
plot_model(model3, type = "eff", terms = "SPECIES",
colors = c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442")) +
theme(legend.text = element_text(face = "italic"))